From affinity selection to kinetic selection in Germinal Centre modelling.

Affinity maturation is an evolutionary process by which the affinity of antibodies (Abs) against specific antigens (Ags) increases through rounds of B-cell proliferation, somatic hypermutation, and positive selection in germinal centres (GC). The positive selection of B cells depends on affinity, but the underlying mechanisms of affinity discrimination and affinity-based selection are not well understood. It has been suggested that selection in GC depends on both rapid binding of B-cell receptors (BcRs) to Ags which is kinetically favourable and tight binding of BcRs to Ags, which is thermodynamically favourable; however, it has not been shown whether a selection bias for kinetic properties is present in the GC. To investigate the GC selection bias towards rapid and tight binding, we developed an agent-based model of GC and compared the evolution of founder B cells with initially identical low affinities but with different association/dissociation rates for Ag presented by follicular dendritic cells in three Ag collection mechanisms. We compared an Ag collection mechanism based on association/dissociation rates of B-cell interaction with presented Ag, which includes a probabilistic rupture of bonds between the B-cell and Ag (Scenario-1) with a reference scenario based on an affinity-based Ag collection mechanism (Scenario-0). Simulations showed that the mechanism of Ag collection affects the GC dynamics and the GC outputs concerning fast/slow (un)binding of B cells to FDC-presented Ags. In particular, clones with lower dissociation rates outcompete clones with higher association rates in Scenario-1, while remaining B cells from clones with higher association rates reach higher affinities. Accordingly, plasma cell and memory B cell populations were biased towards B-cell clones with lower dissociation rates. Without such probabilistic ruptures during the Ag extraction process (Scenario-2), the selective advantage for clones with very low dissociation rates diminished, and the affinity maturation level of all clones decreased to the reference level.

Human Blood Lipoprotein Predictions from 1H NMR Spectra: Protocol, Model Performances, and Cage of Covariance.

Lipoprotein subfractions are biomarkers for the early diagnosis of cardiovascular diseases. The reference method, ultracentrifugation, for measuring lipoproteins is time-consuming, and there is a need to develop a rapid method for cohort screenings. This study presents partial least-squares regression models developed using 1H nuclear magnetic resonance (NMR) spectra and concentrations of lipoproteins as measured by ultracentrifugation on 316 healthy Danes. This study explores, for the first time, different regions of the 1H NMR spectrum representing signals of molecules in lipoprotein particles and different lipid species to develop parsimonious, reliable, and optimal prediction models. A total of 65 lipoprotein main and subfractions were predictable with high accuracy, Q2 of >0.6, using an optimal spectral region (1.4-0.6 ppm) containing methylene and methyl signals from lipids. The models were subsequently tested on an independent cohort of 290 healthy Swedes with predicted and reference values matching by up to 85-95%. In addition, an open software tool was developed to predict lipoproteins concentrations in human blood from standardized 1H NMR spectral recordings.

Repeatability and reproducibility of lipoprotein particle profile measurements in plasma samples by ultracentrifugation.

Background Characterization of lipoprotein particle profiles (LPPs) (including main classes and subclasses) by means of ultracentrifugation (UC) is highly requested given its clinical potential. However, rapid methods are required to replace the very labor-intensive UC method and one solution is to calibrate rapid nuclear magnetic resonance (NMR)-based prediction models, but the reliability of the UC-response method required for the NMR calibration has been largely overlooked. Methods This study provides a comprehensive repeatability and reproducibility study of various UC-based lipid measurements (cholesterol, triglycerides [TGs], free cholesterol, phospholipids, apolipoprotein [apo]A1 and apoB) in different main classes and subclasses of 25 duplicated fresh plasma samples and of 42 quality control (QC) frozen pooled plasma samples of healthy individuals. Results Cholesterol, apoA1 and apoB measurements were very repeatable in all classes (intraclass correlation coefficient [ICC]: 92.93%-99.54%). Free cholesterol and phospholipid concentrations in main classes and subclasses and TG concentrations in high-density lipoproteins (HDL), HDL subclasses and low-density lipoproteins (LDL) subclasses, showed worse repeatability (ICC: 19.21%-99.08%) attributable to low concentrations, variability introduced during UC and assay limitations. On frozen QC samples, the reproducibility of cholesterol, apoA1 and apoB concentrations was found to be better than for the free cholesterol, phospholipids and TGs concentrations. Conclusions This study shows that for LPPs measurements near or below the limit of detection (LOD) in some of the subclasses, as well as the use of frozen samples, results in worsened repeatability and reproducibility. Furthermore, we show that the analytical assay coupled to UC for free cholesterol and phospholipids have different repeatability and reproducibility. All of this needs to be taken into account when calibrating future NMR-based models.

Proof of concept study of age-dependent DNA methylation markers across different tissues by massive parallel sequencing.

The use of DNA methylation (DNAm) for chronological age determination has been widely investigated within the last few years for its application within the field of forensic genetics. The majority of forensic studies are based on blood, saliva, and buccal cell samples, respectively. Although these types of samples represent an extensive amount of traces found at a crime scene or are readily available from individuals, samples from other tissues can be relevant for forensic investigations. Age determination could be important for cases involving unidentifiable bodies and based on remaining soft tissue e.g. brain and muscle, or completely depend on hard tissue such as bone. However, due to the cell type specificity of DNAm, it is not evident whether cell type specific age-dependent CpG positions are also applicable for age determination in other cell types. Within this pilot study, we investigated whether 13 previously selected age-dependent loci based on whole blood analysis including amongst others ELOVL2, TRIM59, F5, and KLF14 also have predictive value in other forensically relevant tissues. Samples of brain, bone, muscle, buccal swabs, and whole blood of 29 deceased individuals (age range 0-87 years) were analyzed for these 13 age-dependent markers using massive parallel sequencing. Seven of these loci did show age-dependency in all five tissues. The change of DNAm during lifetime was different in the set of tissues analyzed, and sometimes other CpG sites within the loci showed a higher age-dependency. This pilot study shows the potential of existing blood DNAm markers for age-determination to analyze other tissues than blood. We identified seven known blood-based DNAm markers for use in muscle, brain, bone, buccal swabs, and blood. Nevertheless, a different reference set for each tissue is needed to adapt for tissue-specific changes of the DNAm over time.

Training for translation between disciplines: a philosophy for life and data sciences curricula.

Motivation: Our society has become data-rich to the extent that research in many areas has become impossible without computational approaches. Educational programmes seem to be lagging behind this development. At the same time, there is a growing need not only for strong data science skills, but foremost for the ability to both translate between tools and methods on the one hand, and application and problems on the other. Results: Here we present our experiences with shaping and running a masters' programme in bioinformatics and systems biology in Amsterdam. From this, we have developed a comprehensive philosophy on how translation in training may be achieved in a dynamic and multidisciplinary research area, which is described here. We furthermore describe two requirements that enable translation, which we have found to be crucial: sufficient depth and focus on multidisciplinary topic areas, coupled with a balanced breadth from adjacent disciplines. Finally, we present concrete suggestions on how this may be implemented in practice, which may be relevant for the effectiveness of life science and data science curricula in general, and of particular interest to those who are in the process of setting up such curricula. Supplementary information: Supplementary data are available at Bioinformatics online.

Dynamic elementary mode modelling of non-steady state flux data.

BACKGROUND: A novel framework is proposed to analyse metabolic fluxes in non-steady state conditions, based on the new concept of dynamic elementary mode (dynEM): an elementary mode activated partially depending on the time point of the experiment. RESULTS: Two methods are introduced here: dynamic elementary mode analysis (dynEMA) and dynamic elementary mode regression discriminant analysis (dynEMR-DA). The former is an extension of the recently proposed principal elementary mode analysis (PEMA) method from steady state to non-steady state scenarios. The latter is a discriminant model that permits to identify which dynEMs behave strongly different depending on the experimental conditions. Two case studies of Saccharomyces cerevisiae, with fluxes derived from simulated and real concentration data sets, are presented to highlight the benefits of this dynamic modelling. CONCLUSIONS: This methodology permits to analyse metabolic fluxes at early stages with the aim of i) creating reduced dynamic models of flux data, ii) combining many experiments in a single biologically meaningful model, and iii) identifying the metabolic pathways that drive the organism from one state to another when changing the environmental conditions.

Global testing of shifts in metabolic phenotype.

INTRODUCTION: Current metabolomics approaches to unravel impact of diet- or lifestyle induced phenotype variation and shifts predominantly deploy univariate or multivariate approaches, with a posteriori interpretation at pathway level. This however often provides only a fragmented view on the involved metabolic pathways. OBJECTIVES: To demonstrate the feasibility of using Goeman's global test (GGT) for assessment of variation and shifts in metabolic phenotype at the level of a priori defined pathways. METHODS: Two intervention studies with identified phenotype variations and shifts were examined. In a weight loss (WL) intervention study obese subjects received a mixed meal challenge before and after WL. In a polyphenol (PP) intervention study obese subjects received a high fat mixed meal challenge (61E% fat) before and after a PP intervention. Plasma samples were obtained at fasting and during the postprandial response. Besides WL- and PP-induced phenotype shifts, also correlation of plasma metabolome with phenotype descriptors was assessed at pathway level. The plasma metabolome covered organic acids, amino acids, biogenic amines, acylcarnitines and oxylipins. RESULTS: For the population of the WL study, GGT revealed that HOMA correlated with the fasting levels of the TCA cycle, BCAA catabolism, the lactate, arginine-proline and phenylalanine-tyrosine pathways. For the population of the PP study, HOMA correlated with fasting metabolite levels of TCA cycle, fatty acid oxidation and phenylalanine-tyrosine pathways. These correlations were more pronounced for metabolic pathways in the fasting state, than during the postprandial response. The effect of the WL and PP intervention on a priori defined metabolic pathways, and correlation of pathways with insulin sensitivity as described by HOMA was in line with previous studies. CONCLUSION: GGT confirmed earlier biological findings in a hypothesis led approach. A main advantage of GGT is that it provides a direct view on involvement of a priori defined pathways in phenotype shifts.

Forensic DNA methylation profiling from minimal traces: How low can we go?

Analysis of human DNA methylation (DNAm) can provide additional investigative leads in crime cases, e.g. the type of tissue or body fluid, the chronological age of an individual, and differentiation between identical twins. In contrast to the genetic profile, the DNAm level is not the same in every cell. At the single cell level, DNAm represents a binary event at a defined CpG site (methylated versus non-methylated). The DNAm level from a DNA extract however represents the average level of methylation of the CpG of interest of all molecules in the forensic sample. The variance of DNAm levels between replicates is often attributed to technological issues, i.e. degradation of DNA due to bisulfite treatment, preferential amplification of DNA, and amplification failure. On the other hand, we show that stochastic variations can lead to gross fluctuation in the analysis of methylation levels in samples with low DNA levels. This stochasticity in DNAm results is relevant since low DNA amounts (1pg - 1ng) is rather the norm than the exception when analyzing forensic DNA samples. This study describes a conceptual analysis of DNAm profiling and its dependence on the amount of input DNA. We took a close look at the variation of DNAm analysis due to DNA input and its consequences for different DNAm-based forensic applications. As can be expected, the 95%-confidence interval of measured DNAm becomes narrower with increasing amounts of DNA. We compared this aspect for two different DNAm-based forensic applications: body fluid identification and chronological age determination. Our study shows that DNA amount should be well considered when using DNAm for forensic applications.

Stoichiometry, Absolute Abundance, and Localization of Proteins in the Bacillus cereus Spore Coat Insoluble Fraction Determined Using a QconCAT Approach.

Spores of Bacillus cereus pose a threat to food safety due to their high resistance to the heat or acid treatments commonly used to make food microbiologically safe. Spores may survive these treatments and later resume growth either on foodstuffs or, after ingestion, upon entering the gut they are capable of producing toxins, which cause either vomiting or diarrhea. The outer layers of the spore, the spore coat and exosporium, consist primarily of proteins that may serve as potential biomarkers for detection. The major morphogenetic protein CotE is important for correct assembly and attachment of the outermost layer, the exosporium, and by extension retention of many proteins. However, characterization of the proteins affected by deletion of CotE has been limited to electrophoretic patterns. Here we report the effect of CotE deletion on the insoluble fraction of the spore proteome through liquid chromatography-Fourier transform tandem mass spectrometry (LC-FTMS/MS) analysis. A total of 560 proteins have been identified in both mutant and wild-type spore coat isolates. A further 163 proteins were identified exclusively in wild-type spore isolates indicating that they are dependent on CotE for their association with the spore. Several of these are newly confirmed as associated with the exosporium, namely BC_2569 (BclF), BC_3345, BC_2427, BC_2878, BC_0666, BC_2984, BC_3481, and BC_2570. A total of 153 proteins were only identified in ΔCotE spore isolates. This was observed for proteins that are known or likely to be interacting with or are encased by CotE. Crucial spore proteins were quantified using a QconCAT reference standard, the first time this was used in a biochemically heterogeneous system. This allowed us to determine the absolute abundance of 21 proteins, which spanned across three orders of magnitude and together covered 5.66% ± 0.51 of the total spore weight. Applying the QconCAT methodology to the ΔCotE mutant allowed us to quantify 4.13% ± 0.14 of the spore total weight and revealed a reduction in abundance for most known exosporium associated proteins upon CotE deletion. In contrast, several proteins, either known or likely to be interacting with or encased by CotE (i.e., GerQ), were more abundant. The results obtained provide deeper insight into the layered spore structure such as which proteins are exposed on the outside of the spore. This information is important for developing detection methods for targeting spores in a food safety setting. Furthermore, protein stoichiometry and determination of the abundance of germination mediating enzymes provides useful information for germination and outgrowth model development.

Chronological age prediction based on DNA methylation: Massive parallel sequencing and random forest regression.

The use of DNA methylation (DNAm) to obtain additional information in forensic investigations showed to be a promising and increasing field of interest. Prediction of the chronological age based on age-dependent changes in the DNAm of specific CpG sites within the genome is one such potential application. Here we present an age-prediction tool for whole blood based on massive parallel sequencing (MPS) and a random forest machine learning algorithm. MPS allows accurate DNAm determination of pre-selected markers and neighboring CpG-sites to identify the best age-predictive markers for the age-prediction tool. 15 age-dependent markers of different loci were initially chosen based on publicly available 450K microarray data, and 13 finally selected for the age tool based on MPS (DDO, ELOVL2, F5, GRM2, HOXC4, KLF14, LDB2, MEIS1-AS3, NKIRAS2, RPA2, SAMD10, TRIM59, ZYG11A). Whole blood samples of 208 individuals were used for training of the algorithm and a further 104 individuals were used for model evaluation (age 18-69). In the case of KLF14, LDB2, SAMD10, and GRM2, neighboring CpG sites and not the initial 450K sites were chosen for the final model. Cross-validation of the training set leads to a mean absolute deviation (MAD) of 3.21 years and a root-mean square error (RMSE) of 3.97 years. Evaluation of model performance using the test set showed a comparable result (MAD 3.16 years, RMSE 3.93 years). A reduced model based on only the top 4 markers (ELOVL2, F5, KLF14, and TRIM59) resulted in a RMSE of 4.19 years and MAD of 3.24 years for the test set (cross validation training set: RMSE 4.63 years, MAD 3.64 years). The amplified region was additionally investigated for occurrence of SNPs in case of an aberrant DNAm result, which in some cases can be an indication for a deviation in DNAm. Our approach uncovered well-known DNAm age-dependent markers, as well as additional new age-dependent sites for improvement of the model, and allowed the creation of a reliable and accurate epigenetic tool for age-prediction without restriction to a linear change in DNAm with age.

Toward Reliable Lipoprotein Particle Predictions from NMR Spectra of Human Blood: An Interlaboratory Ring Test.

Lipoprotein profiling of human blood by 1H nuclear magnetic resonance (NMR) spectroscopy is a rapid and promising approach to monitor health and disease states in medicine and nutrition. However, lack of standardization of measurement protocols has prevented the use of NMR-based lipoprotein profiling in metastudies. In this study, a standardized NMR measurement protocol was applied in a ring test performed across three different laboratories in Europe on plasma and serum samples from 28 individuals. Data was evaluated in terms of (i) spectral differences, (ii) differences in LPD predictions obtained using an existing prediction model, and (iii) agreement of predictions with cholesterol concentrations in high- and low-density lipoproteins (HDL and LDL) particles measured by standardized clinical assays. ANOVA-simultaneous component analysis (ASCA) of the ring test spectral ensemble that contains methylene and methyl peaks (1.4-0.6 ppm) showed that 97.99% of the variance in the data is related to subject, 1.62% to sample type (serum or plasma), and 0.39% to laboratory. This interlaboratory variation is in fact smaller than the maximum acceptable intralaboratory variation on quality control samples. It is also shown that the reproducibility between laboratories is good enough for the LPD predictions to be exchangeable when the standardized NMR measurement protocol is followed. With the successful implementation of this protocol, which results in reproducible prediction of lipoprotein distributions across laboratories, a step is taken toward bringing NMR more into scope of prognostic and diagnostic biomarkers, reducing the need for less efficient methods such as ultracentrifugation or high-performance liquid chromatography (HPLC).

Genome-wide mapping of transcriptional enhancer candidates using DNA and chromatin features in maize.

BACKGROUND: While most cells in multicellular organisms carry the same genetic information, in each cell type only a subset of genes is being transcribed. Such differentiation in gene expression depends, for a large part, on the activation and repression of regulatory sequences, including transcriptional enhancers. Transcriptional enhancers can be located tens of kilobases from their target genes, but display characteristic chromatin and DNA features, allowing their identification by genome-wide profiling. Here we show that integration of chromatin characteristics can be applied to predict distal enhancer candidates in Zea mays, thereby providing a basis for a better understanding of gene regulation in this important crop plant. RESULT: To predict transcriptional enhancers in the crop plant maize (Zea mays L. ssp. mays), we integrated available genome-wide DNA methylation data with newly generated maps for chromatin accessibility and histone 3 lysine 9 acetylation (H3K9ac) enrichment in young seedling and husk tissue. Approximately 1500 intergenic regions, displaying low DNA methylation, high chromatin accessibility and H3K9ac enrichment, were classified as enhancer candidates. Based on their chromatin profiles, candidate sequences can be classified into four subcategories. Tissue-specificity of enhancer candidates is defined based on the tissues in which they are identified and putative target genes are assigned based on tissue-specific expression patterns of flanking genes. CONCLUSIONS: Our method identifies three previously identified distal enhancers in maize, validating the new set of enhancer candidates and enlarging the toolbox for the functional characterization of gene regulation in the highly repetitive maize genome.

Towards a Hierarchical Strategy to Explore Multi-Scale IP/MS Data for Protein Complexes.

Protein interaction in cells can be described at different levels. At a low interaction level, proteins function together in small, stable complexes and at a higher level, in sets of interacting complexes. All interaction levels are crucial for the living organism, and one of the challenges in proteomics is to measure the proteins at their different interaction levels. One common method for such measurements is immunoprecipitation followed by mass spectrometry (IP/MS), which has the potential to probe the different protein interaction forms. However, IP/MS data are complex because proteins, in their diverse interaction forms, manifest themselves in different ways in the data. Numerous bioinformatic tools for finding protein complexes in IP/MS data are currently available, but most tools do not provide information about the interaction level of the discovered complexes, and no tool is geared specifically to unraveling and visualizing these different levels. We present a new bioinformatic tool to explore IP/MS datasets for protein complexes at different interaction levels and show its performance on several real-life datasets. Our tool creates clusters that represent protein complexes, but unlike previous methods, it arranges them in a tree-shaped structure, reporting why specific proteins are predicted to build a complex and where it can be divided into smaller complexes. In every data analysis method, parameters have to be chosen. Our method can suggest values for its parameters and comes with adapted visualization tools that display the effect of the parameters on the result. The tools provide fast graphical feedback and allow the user to interact with the data by changing the parameters and examining the result. The tools also allow for exploring the different organizational levels of the protein complexes in a given dataset. Our method is available as GNU-R source code and includes examples at www.bdagroup.nl.

Scaling in ANOVA-simultaneous component analysis.

In omics research often high-dimensional data is collected according to an experimental design. Typically, the manipulations involved yield differential effects on subsets of variables. An effective approach to identify those effects is ANOVA-simultaneous component analysis (ASCA), which combines analysis of variance with principal component analysis. So far, pre-treatment in ASCA received hardly any attention, whereas its effects can be huge. In this paper, we describe various strategies for scaling, and identify a rational approach. We present the approaches in matrix algebra terms and illustrate them with an insightful simulated example. We show that scaling directly influences which data aspects are stressed in the analysis, and hence become apparent in the solution. Therefore, the cornerstone for proper scaling is to use a scaling factor that is free from the effect of interest. This implies that proper scaling depends on the effect(s) of interest, and that different types of scaling may be proper for the different effect matrices. We illustrate that different scaling approaches can greatly affect the ASCA interpretation with a real-life example from nutritional research. The principle that scaling factors should be free from the effect of interest generalizes to other statistical methods that involve scaling, as classification methods.

Quantitative proteomics analysis of an ethanol- and a lactate-producing mutant strain of Synechocystis sp. PCC6803.

BACKGROUND: This study aimed at exploring the molecular physiological consequences of a major redirection of carbon flow in so-called cyanobacterial cell factories: quantitative whole-cell proteomics analyses were carried out on two (14)N-labelled Synechocystis mutant strains, relative to their (15)N-labelled wild-type counterpart. Each mutant strain overproduced one specific commodity product, i.e. ethanol or lactic acid, to such an extent that the majority of the incoming CO2 in the organism was directly converted into the product. RESULTS: In total, 267 proteins have been identified with a significantly up- or down-regulated expression level. In the ethanol-producing mutant, which had the highest relative direct flux of carbon-to-product (>65%), significant up-regulation of several components involved in the initial stages of CO2 fixation for cellular metabolism was detected. Also a general decrease in abundance of the protein synthesizing machinery of the cells and a specific induction of an oxidative stress response were observed in this mutant. In the lactic acid overproducing mutant, that expresses part of the heterologous l-lactate dehydrogenase from a self-replicating plasmid, specific activation of two CRISPR associated proteins, encoded on the endogenous pSYSA plasmid, was observed. RT-qPCR was used to measure, of nine of the genes identified in the proteomics studies, also the adjustment of the corresponding mRNA level. CONCLUSION: The most striking adjustments detected in the proteome of the engineered cells were dependent on the specific product formed, with, e.g. more stress caused by lactic acid- than by ethanol production. Up-regulation of the total capacity for CO2 fixation in the ethanol-producing strain was due to hierarchical- rather than metabolic regulation. Furthermore, plasmid-based expression of heterologous gene(s) may induce genetic instability. For selected, limited, number of genes a striking correlation between the respective mRNA- and the corresponding protein expression level was observed, suggesting that for the expression of these genes regulation takes place primarily at the level of gene transcription.

Validation and selection of ODE based systems biology models: how to arrive at more reliable decisions.

BACKGROUND: Most ordinary differential equation (ODE) based modeling studies in systems biology involve a hold-out validation step for model validation. In this framework a pre-determined part of the data is used as validation data and, therefore it is not used for estimating the parameters of the model. The model is assumed to be validated if the model predictions on the validation dataset show good agreement with the data. Model selection between alternative model structures can also be performed in the same setting, based on the predictive power of the model structures on the validation dataset. However, drawbacks associated with this approach are usually under-estimated. RESULTS: We have carried out simulations by using a recently published High Osmolarity Glycerol (HOG) pathway from S.cerevisiae to demonstrate these drawbacks. We have shown that it is very important how the data is partitioned and which part of the data is used for validation purposes. The hold-out validation strategy leads to biased conclusions, since it can lead to different validation and selection decisions when different partitioning schemes are used. Furthermore, finding sensible partitioning schemes that would lead to reliable decisions are heavily dependent on the biology and unknown model parameters which turns the problem into a paradox. This brings the need for alternative validation approaches that offer flexible partitioning of the data. For this purpose, we have introduced a stratified random cross-validation (SRCV) approach that successfully overcomes these limitations. CONCLUSIONS: SRCV leads to more stable decisions for both validation and selection which are not biased by underlying biological phenomena. Furthermore, it is less dependent on the specific noise realization in the data. Therefore, it proves to be a promising alternative to the standard hold-out validation strategy.

Time-series analysis of the transcriptome and proteome of Escherichia coli upon glucose repression.

Time-series transcript- and protein-profiles were measured upon initiation of carbon catabolite repression in Escherichia coli, in order to investigate the extent of post-transcriptional control in this prototypical response. A glucose-limited chemostat culture was used as the CCR-free reference condition. Stopping the pump and simultaneously adding a pulse of glucose, that saturated the cells for at least 1h, was used to initiate the glucose response. Samples were collected and subjected to quantitative time-series analysis of both the transcriptome (using microarray analysis) and the proteome (through a combination of 15N-metabolic labeling and mass spectrometry). Changes in the transcriptome and corresponding proteome were analyzed using statistical procedures designed specifically for time-series data. By comparison of the two sets of data, a total of 96 genes were identified that are post-transcriptionally regulated. This gene list provides candidates for future in-depth investigation of the molecular mechanisms involved in post-transcriptional regulation during carbon catabolite repression in E. coli, like the involvement of small RNAs.

Strategies for individual phenotyping of linoleic and arachidonic acid metabolism using an oral glucose tolerance test.

The ability to restore homeostasis upon environmental challenges has been proposed as a measure for health. Metabolic profiling of plasma samples during the challenge response phase should offer a profound view on the flexibility of a phenotype to cope with daily stressors. Current data modeling approaches, however, struggle to extract biological descriptors from time-resolved metabolite profiles that are able to discriminate between different phenotypes. Thus, for the case of oxylipin responses in plasma upon an oral glucose tolerance test we developed a modeling approach that incorporates a priori biological pathway knowledge. The degradation pathways of arachidonic and linoleic acids were modeled using a regression model based on a pseudo-steady-state approximated model, resulting in a parameter A that summarizes the relative enzymatic activity in these pathways. Analysis of the phenotypic parameters As suggests that different phenotypes can be discriminated according to preferred relative activity of the arachidonic and linoleic pathway. Correlation analysis shows that there is little or no competition between the arachidonic and linoleic acid pathways, although they share the same enzymes.

MetDFBA: incorporating time-resolved metabolomics measurements into dynamic flux balance analysis.

Understanding cellular adaptation to environmental changes is one of the major challenges in systems biology. To understand how cellular systems react towards perturbations of their steady state, the metabolic dynamics have to be described. Dynamic properties can be studied with kinetic models but development of such models is hampered by limited in vivo information, especially kinetic parameters. Therefore, there is a need for mathematical frameworks that use a minimal amount of kinetic information. One of these frameworks is dynamic flux balance analysis (DFBA), a method based on the assumption that cellular metabolism has evolved towards optimal changes to perturbations. However, DFBA has some limitations. It is less suitable for larger systems because of the high number of parameters to estimate and the computational complexity. In this paper, we propose MetDFBA, a modification of DFBA, that incorporates measured time series of both intracellular and extracellular metabolite concentrations, in order to reduce both the number of parameters to estimate and the computational complexity. MetDFBA can be used to estimate dynamic flux profiles and, in addition, test hypotheses about metabolic regulation. In a first case study, we demonstrate the validity of our method by comparing our results to flux estimations based on dynamic 13C MFA measurements, which we considered as experimental reference. For these estimations time-resolved metabolomics data from a feast-famine experiment with Penicillium chrysogenum was used. In a second case study, we used time-resolved metabolomics data from glucose pulse experiments during aerobic growth of Saccharomyces cerevisiae to test various metabolic objectives.